Augmented reality method applied to the integration of a pair of spectacles into an image of a face

ABSTRACT

Method for creating a final real-time photorealistic image of a virtual object, corresponding to a real object arranged on an original photo of a user, in a realistic orientation related to the user&#39;s position, includes: detecting the presence of an area for the object in the photo; determining the position of characteristic points of the area for the object in the photo; determining the 3D orientation of the face, the angles Φ and Ψ of the camera having taken the photo relative to the principal plane of the area; selecting the texture to be used for the virtual object, in accordance with the angle-of-view, and generating the view of the virtual object in 3D; creating a first layered rendering in the correct position consistent with the position of the placement area for the object in the original photo; obtaining the photorealistic rendering by adding overlays to obtain the final image.

This invention relates to the field of image processing and imagesynthesis. It relates more specifically to the real-time integration ofa virtual object into photographs or videos.

BACKGROUND OF THE INVENTION AND PROBLEM STATEMENT

The context of the invention is the real-time virtual trying on of anobject in the most realistic way possible; typically these objects are apair of spectacles to be integrated into a photograph or a videorepresenting the face of a person oriented substantially facing thecamera.

The growth in Internet sales, a limited stock, or any other reasonpreventing or hindering the actual trying on of a real object, generatea need for the virtual trying on of this object. Current solutions,based on a virtual reality or augmented reality, are insufficient in thecase of spectacles since they lack realism or interactivity. In additionmost of the time they require a lot of data and lots of computing time.

OBJECTIVE OF THE INVENTION

The objective of this invention is to propose a method for modelingvirtual spectacles representative of real spectacles and a method ofintegrating in real time these said virtual spectacles into a photographor a video representing the face of a person, limiting the number ofnecessary data.

“Integration” means a positioning and realistic rendering of thesevirtual spectacles on a photo or a video representing a person withoutspectacles, thus generating a new photo or video equivalent to the photoor video of the individual that would have been obtained byphotographing or filming the same person wearing the real spectaclescorresponding to these virtual spectacles.

DESCRIPTION OF THE INVENTION

The invention envisages in the first place a method of creating areal-time photorealistic final image of a virtual object, correspondingto a real object, arranged on an original photo of a person in arealistic orientation linked to the position of said user, characterizedin that it comprises the following steps:

-   -   510: detecting the presence of a placement area for the object        in an original photo,    -   530: determining the position of characteristic points of the        placement area for the object in the original photo,    -   540: determining the 3D orientation of the face, i.e. the angles        φ and ψ of the camera having taken the photo, relative to the        principal plane of the placement area for the object,    -   550: selecting the texture to be used for the virtual object, in        accordance with the angle-of-view, and generating the view of        the virtual object in the 3D (φ, ψ)/2D (⊕, s) position in        question,    -   560: creating a first rendering by establishing a layered        rendering in the correct position consistent with the position        of the placement area for the object in the original photo,    -   570: obtaining the photorealistic rendering by adding overlays,        referred to as semantic overlays, so as to obtain the final        image.

According to a particular implementation of the method, the object is apair of spectacles and the placement area is the user's face.

In that case, according to an advantageous implementation, step 510 usesa first boosting algorithm AD1 trained to determine whether the originalphoto contains a face.

In a particular implementation of the method as described, step 530consists of:

-   -   determining a similarity β, to be applied to an original photo,        to obtain a face similar to a reference face in magnification        and orientation, and    -   determining the position of the precise exterior corner A and        the precise interior point B for each eye in the face of the        original photo.

More specifically, in this case, step 530 advantageously uses aniterative algorithm that makes it possible to refine the value of thesimilarity β and the positions of the characteristic points:

-   -   defining the first parameters of similarity β₀=(tx₀, ty₀, s₀,        ⊕₀),    -   characterizing the eyes in the original photo 1 of the user,        from a predefined set of models of eyes DB_(models) _(—) _(eyes)        and evaluating the scale,    -   re-evaluating the parameters of similarity β₁=(tx₁, ty₁, s₁,        ⊕₁).

According to a particular implementation of the method, step 530 uses asecond boosting algorithm trained with an eyes learning database,comprising a set of positive examples of eyes and a set of negativeexamples of eyes.

In a particular implementation of the method as described, step 550consists of:

-   -   1/ determining a simplified geometric model of the real pair of        spectacles, said model comprising a predefined number N of        surfaces and their normals, taking as the orientation of these        normals the exterior of the envelop convex to the real pair of        spectacles,    -   2/ applying to it, from a predefined set of reference        orientations, an orientation closest to angles φ and ψ,    -   3/ calculating a texture of the simplified geometric model,        positioned in the 3D orientation of the reference orientation        closest to angles φ and ψ, using the texture of this reference        orientation; this is equivalent to texturing each of the N        surfaces of the simplified geometric model while classifying the        surface in the current view into three classifications: interior        surface of the frame, exterior frame of the frame, lens.

In this case, according to a more particular implementation, thesimplified geometric model of a real pair of spectacles, consisting of aframe and lenses, is obtained in a phase 100 in which:

-   -   a set of shots of the real pair of spectacles to be modeled is        produced, with different angles-of-view and using different        screen backgrounds with and without the real pair of spectacles,    -   the simplified geometric model is constructed, consisting of a        number N of surfaces surface; and their normal {right arrow over        (n)}_(j), beginning with a not very dense surface mesh and using        an optimization algorithm that deforms the model's mesh so that        the projections of its silhouette in each of the views best        match the silhouettes detected in the images.

According to an advantageous embodiment, the number N of surfaces of thesimplified geometric model is a value close to twenty.

According to a particular implementation of the method, phase 100 alsocomprises a step 110 consisting of obtaining images of the real pair ofspectacles; the lens must match the lens intended for trying on 500, andin this step 110:

-   -   the real pair of spectacles is photographed at high resolution        according to V different reference orientations Orientation^(i)        and in N light configurations showing the transmission and        reflection of the spectacle lens,    -   these reference orientations are selected by discretizing a        spectrum of orientations corresponding to possible orientations        when spectacles are tried on,    -   V*N high-resolution images of the real pair of spectacles,        designated Image-spectacles^(i,j), are obtained.

In this case, according to a particular implementation, the number V ofreference orientations is equal to nine, and if an orthogonal referencespace with axes x, y, z is defined, where the y-axis corresponds to thevertical axis, ψ to the angle of rotation around the x-axis, φ to theangle of rotation around the y-axis, the V positions Orientation^(i)selected are such that the angle ψ substantially takes the respectivevalues −16°, 0° or 16°, the angle φ takes the respective values −16°, 0°or 16°.

According to a particular implementation of the method:

-   -   the first light configuration respects the colors and materials        of the real pair of spectacles, using neutral light conditions;        the V high-resolution transmission images Transmission^(i)        created in this light configuration allow the maximum        transmission of light through the lenses to be revealed,    -   the second light configuration highlights the geometric        characteristics of the real pair of spectacles (4), using        conditions of intense reflection; the V high-resolution        reflection images Reflection^(i) obtained in this second light        configuration reveal the physical reflection properties of the        lens.

According to a particular implementation of the method, phase 100comprises a step 120 of creating a texture overlay of the frameFrame^(i), for each of the V reference orientations.

In this case, more specifically in this step 120:

-   -   for each of the V reference orientations, the high-resolution        reflection image Reflection^(i) is taken,    -   a binary image is generated with the same resolution as the        high-resolution reflection image of the reference orientations;        said binary image is called the lens silhouette Lens^(i)        _(binary). in this lens silhouette Lens^(i) _(binary), the value        of the pixel is equal to one if the pixel represents the lenses        and zero otherwise.

Even more particularly, the shape of the lenses needed to generate thelens silhouette Lens^(i) _(binary) is extracted using an active contoursalgorithm based on the assumption that the frame and the lenses havedifferent transparencies.

According to an advantageous implementation, in step 120:

-   -   a lens overlay Lens^(i) _(overlay) is generated for each of the        reference orientations by copying, for each pixel with a value        equal to one in the binary overlay of the lens Lens^(i)        _(binary), the information contained in the high-resolution        reflection image and assigning zero to the other pixels,

this lens overlay Lens^(i) _(overlay) is a high-definition cropped imageof the lens using, for cropping the original high-definition image, thelens silhouette Lens^(i) _(binary).

-   -   the associated high-resolution reflection image Reflection^(i)        is selected for each of the reference orientations, and a binary        background image Background^(i) _(binary) is generated by        automatically extracting the background,    -   a binary image is generated from the binary overlay of the frame        Frame^(i) _(binary), by deducting from a neutral image the        outline image of the lenses and the outline image of the        background,    -   a texture overlay of the frame behind the lens Frame^(i)        _(behind) _(—) _(lens), with the texture of the frame        corresponding to the portion of the frame located behind the        lenses, is generated for each of the reference orientations by        copying, for each pixel with a value equal to one in the binary        lens overlay Lens^(i) _(binary), the information contained in        the high-resolution transmission image Transmission^(i), and        assigning zero to the other pixels,    -   a texture overlay of the frame outside the lens Frame^(i)        _(exterior) _(—) _(lens) is generated by copying, for each pixel        with a value equal to one in the binary frame overlay Frame^(i)        _(binary) the information contained in the high-resolution        reflection image, and assigning zero to the other pixels,    -   an overlay of the texture of the frame Frame^(i) is defined as        the sum of the overlay of the texture of the frame behind the        lens Frame^(i) _(behind) _(—) _(lens) and the overlay of the        texture of the frame outside the lens Frame^(i) _(exterior) _(—)        _(lens).

According to a particular implementation, in step 550, the texturecalculation is performed using overlays associated to the referenceorientation closest to angles φ and ψ, by the following sub-steps:

-   -   inversion of the normals {right arrow over (n)}_(j) of each of        the surfaces of the pair of spectacles modeled surface_(j) and        projection of the frame overlay Frame^(i), limited to the lens        space of the reference orientation closest to angles φ and ψ, to        obtain a texture overlay of the internal surface of the frame        TextureFrame^(i) _(surface) _(—) _(interior). that makes it        possible to structure the arms of the frame seen through the        lens, in a textured reference model, oriented according to the        reference orientation closest to angles φ and ψ,    -   projection of the frame overlay Frame^(i), limited to the space        outside the lens of the reference orientation closest to angles        φ and ψ, to obtain a texture overlay of the external surface of        the frame TextureFrame^(i) _(surface) _(—) _(exterior) that        makes it possible to structure the surfaces of the frame outside        the lens, in the textured reference model, oriented according to        the reference orientation closest to angles φ and ψ,    -   projection of the lens overlay limited to the lens to obtain a        lens texture overlay TextureLens^(i) that makes it possible to        structure the lens, in the textured reference model, oriented        according to the reference orientation closest to angles φ and        ψ.

According to a particular implementation of the method as described,step 560 consists of generating an oriented textured model, orientedaccording to angles φ and ψ and according to the scale and orientationof the original photo, from a textured reference model, orientedaccording to the reference orientation closest to angles φ and ψ, andparameters of similarity β; this step comprises the following sub-steps:

-   -   using a bilinear affine interpolation to orient an interpolated        textured model according to the angles φ and ψbased on the        textured reference model oriented according to the reference        orientation closest to these angles φ and ψ,    -   using the similarity β to be applied, so as to obtain the same        scale, the same image orientation and the same centering as the        original photo, thus producing an oriented textured model.

In this case, more specifically, step 560 also comprises a sub-step ofgeometrically varying the arms of the virtual spectacles according tothe morphology of the face of the original photo, so as to obtain aspectacles overlay Spectades_(overlay) of the virtual pair of spectaclesand a binary overlay Spectades_(overlay) _(—) _(binary), oriented as theoriginal photo, and which can therefore be superimposed on it.

According to a particular implementation of the method as described,step 570 consists of taking into account the light interactions due towearing virtual spectacles, particularly the shadows cast onto the face,the visibility of the skin through the lens of the spectacles, thereflection of the environment on the spectacles.

According to a more particular implementation, step 570 comprises thefollowing sub-steps:

-   -   1/ creating a shadow map Visibility^(i) for each reference        orientation, obtained by calculating the light occlusion        produced by the real pair of spectacles on each area of the        average face when the entire face is lit by a light source, said        light source being modeled by a set of point sources emitting in        all directions, located at regular intervals in a rectangle,    -   2/ multiplying the shadow map and the photo to obtain a shadowed        photo overlay, designated L_(skin) _(—) _(Shadowed),    -   3/ blending the shadowed photo overlay L_(skin) _(—) _(Shadowed)        and the spectacles overlay Spectacles_(overlay) by linear        interpolation, depending on the coefficient of opacity α of the        lens in an area limited to the binary overlay of the virtual        pair of spectacles Spectacles_(overlay) _(—) _(binary), to        obtain a final image; this is an image of the original photo on        which an image of the selected model of spectacles is        superimposed, oriented as the original picture and given shadow        properties

According to a particular implementation, the method as describedfurther comprises a phase 200 of creating a database of models of eyesDB_(models) _(—) _(eyes), comprising a plurality of photographs of facesreferred to as learning photographs App_(eyes) ^(k)

In this case, more specifically, phase 200 advantageously comprises thefollowing steps:

-   -   step 210, of defining a reference face shape and orientation by        setting a reference interpupillary distance di₀, by centering        the interpupillary segment on the center of the image and        orienting this interpupillary segment parallel to the image's        horizontal axis,    -   then, for each k^(th) learning photograph App_(eyes) ^(k) not        yet processed:    -   step 230, of determining the precise position of characteristic        points: exterior point B_(l) ^(k), B_(r) ^(k), and interior        point A_(l) ^(k), A_(r) ^(k) of each eye and determining the        respective geometric center G_(l) ^(k), G_(r) ^(k) of these eyes        and the interpupillary distance di^(k),    -   step 231, of transforming this k^(th) learning photograph        App_(eyes) ^(k) into a gray-scale image App_(eyes-gray) ^(k),        and normalizing the gray-scale image by applying a similarity        S^(k)(tx, ty, s, Θ) so as to establish the orientation and scale        of the reference face (7) to obtain a k^(th) gray-scale        normalized learning photograph App_(eyes) _(—) _(gray) _(—)        _(norm) ^(k),    -   step 232, of defining a window of fixed dimensions for each of        the two eyes, in the k^(th) gray-scale normalized learning        photograph App_(eyes) _(—) _(gray) _(—) _(norm) ^(k): left patch        P_(i) ^(k) and right patch P_(r) ^(k); the position of a patch P        is defined by the fixed distance Δ between the exterior point of        the eye B and the edge of the patch P closest to this exterior        point of the eye B    -   step 233, for each of the two patches P_(l) ^(k), P_(r) ^(k)        associated to the k^(th) gray-scale normalized learning        photograph App_(eyes) _(—) _(gray) _(—) _(norm) ^(k), of        normalizing the gray-scales,    -   step 234, for the first learning photograph App_(eyes) ¹, of        storing each of the patches P_(l) ¹, P_(r) ¹, called descriptor        patches, in the eyes database DB_(models) _(—) _(eyes),    -   step 235, for each of the patches P associated to the k^(th)        gray-scale normalized learning photograph App_(eyes) _(—)        _(gray) _(—) _(norm) ^(k), of correlating the corresponding        normalized texture column-vector T0 with each of the normalized        texture column-vectors T0 _(i) of the corresponding descriptor        patches,    -   step 236, of comparing, for each of the patches P_(l) ^(k),        P_(r) ^(k), this correlation measurement with a previously        defined correlation threshold threshold, and, if the correlation        is less than the threshold, of storing patch P as a descriptor        patch in the eyes database DB_(models) _(—) _(eyes).

According to a particular implementation, in this case, in step 232, thefixed distance Δ is chosen so that no texture exterior to the face isincluded in patch P, and the width w and height h of patches P_(l) ^(k),P_(r) ^(k) are constant and predefined, so that patch P contains the eyecorresponding to this patch P in full, and contains no texture that isexterior to the face, irrespective of the learning photograph App_(eyes)^(k).

The invention also envisages in another aspect a computer programproduct comprising program code instructions for executing steps of amethod as described when said program is run on a computer.

BRIEF DESCRIPTION OF THE FIGURES

The description that follows, given solely as an example of anembodiment of the invention, is made with reference to the figuresincluded in an appendix, in which:

FIG. 1 a represents a pair of wraparound sports spectacles,

FIG. 1 b represents an initial mesh used to represent a real pair ofspectacles,

FIG. 1 c illustrates the definition of the normal to the surface in asegment V_(i) ⁺ V_(i),

FIG. 1 d represents a simplified model for a pair of wraparound sportsspectacles,

FIG. 2 illustrates the principle for photographing a real pair ofspectacles for modeling,

FIG. 3 is a schematic of the step for obtaining a simplified geometricmodel,

FIG. 4 represents the nine shots of a pair of spectacles,

FIG. 5 is a schematic of the step for obtaining images of the real pairof spectacles,

FIG. 6 is a schematic of the step for generating overlays of spectacles,

FIGS. 7 a and 7 b illustrate the creation of a shadow map on an averageface,

FIG. 8 is a schematic of the transition between a learning photographand a gray-scale normalized learning photograph,

FIG. 9 is a schematic of the construction of the final image.

DETAILED DESCRIPTION OF A MODE OF IMPLEMENTATION OF THE INVENTION

The method here comprises five phases:

the first phase 100 is a method of modeling real pairs of spectaclesallowing a spectacles database DB_(models) _(—) _(spectacles) of virtualmodels of pairs of spectacles to be populated,

the second phase 200 is a method of creating a database of models ofeyes DB_(models) _(—) _(eyes),

the third phase 300 is a method of searching for criteria forrecognizing a face in a photo.

the fourth phase 400 is a method of searching for criteria forrecognizing characteristic points in a face.

the fifth phase 500, referred to as trying on virtual spectacles, is amethod of generating a final image 5, from a virtual model 3 of a pairof spectacles, and an original photo 1 of a subject taken, in thisexample, by a camera and representing the face 2 of the subject.

The first four phases, 100, 200, 300 and 400, are performed on apreliminary basis, while phase 500 of trying on virtual spectacles isutilized many times, on different subjects and different virtual pairsof spectacles, based on the results from the four preliminary phases.

Phase 100 of Modeling Pairs of Spectacles

To begin with the first phase 100, the modeling of pairs of spectacles,is described:

The purpose of this phase of modeling pairs of spectacles is to model areal pair of spectacles 4 geometrically and texturally. The datacalculated by this spectacles modeling algorithm, for each pair ofspectacles made available during the trying-on phase 500, are stored ina database DB_(models) _(—) _(spectacles) so as to be available duringthis trying-on phase.

This spectacles modeling phase 100 is divided into four steps.

Step 110: Obtaining Images of the Real Pair of Spectacles 4

The procedure for constructing a simplified geometric model 6 of a realpair of spectacles 4, uses a device for taking photographs 50.

This device for taking photographs 50 is, in this example, representedin FIG. 2 and consists of:

-   -   a base 51, which allows the real pair of spectacles 4 to be        modeled to be supported. This base 51 is made of a transparent        material such as transparent plexiglas. This base 51 is formed        of two parts, 51 a and 51 b, which fit together. Part 51 b is        the portion of the base 51 that is in contact with the real pair        of spectacles 4 when this is placed on the base 51. Part 51 b        can be separated from part 51 a and can therefore be chosen from        a set of parts with shapes optimized with respect to the shape        of the object to be placed (goggles, masks, jewelry). Part 51 b        has three contact points with the real pair of spectacles 4,        corresponding to the actual contact points on a face when the        real pair of spectacles 4 is worn, i.e. at the two ears and the        nose.    -   a turntable 52 on which part 51 a of base 51 is fixed, said        turntable 52 being placed on a foot 53; said turntable 52 makes        it possible to rotate the removable base according to a vertical        axis of rotation Z.    -   a vertical rail 54 allowing digital cameras 55 to be attached at        different heights (the number of digital cameras 55 is variable,        from one to eight in this example). The digital cameras 55 are        respectively fixed on the vertical rail 54 by a ball joint        allowing rotation in pitch and yaw. This said vertical rail 54        is positioned at a distance from the foot 53, which is fixed in        this example. The cameras are oriented such that their        respective photographic field contains the real pair of        spectacles 4 to be modeled, when it is placed on part 51 b of        base 51, part 51 b being fitted onto part 51 a.    -   a horizontal rail 56 secured to a vertical mount on which a        screen 58 with a changeable background color 59 is attached. In        this example, screen 58 is an LCD screen. The background color        59 is selected in this example from the colors red, blue, green,        white or neutral, i.e. a gray containing the three colors red,        green, blue in a uniform distribution with a value of two        hundred, for example. Said horizontal rail 56 is positioned such        that the real pair of spectacles 4 to be modeled, placed on part        51 b fitted onto fixed part 51 a on the turntable 52, is between        the screen 58 and the vertical rail 54.    -   possibly a base plate 60 supporting the vertical rail 54, the        foot 53 and the horizontal rail 56.

The device for taking photographs 50 is controlled by a unit associatedto a software system 61. This control consists of managing the positionand orientation of digital cameras 55, relative to the object to bephotographed, assumed to be fixed, for managing the background color 59of the screen 58 and its position, and managing the rotation of theturntable 52.

The device for taking photographs 50 is calibrated by conventionalcalibration procedures in order to accurately know the geometricposition of each of the cameras 55 and the position of the vertical axisof rotation Z.

In this example, calibrating the device for taking photographs 50consists of:

firstly, placing one of the digital cameras 55 sufficiently precisely atthe level of the real pair of spectacles 4 to be modeled, so that itsrespective shot is a frontal view,

secondly, to remove the real pair of spectacles 4 and possibly removablepart 51 b, and place a test chart 57, not necessarily flat, verticallyon the turntable 52. In this non-limiting example, this test chart 57consists of a checkerboard.

-   -   thirdly, to determine the precise position of each digital        camera 55 by a conventional method, using images 62 obtained for        each of the digital cameras 55 with different shots of the test        chart 57, using different screen backgrounds 59.    -   fourthly, to determine the position of the vertical axis of        rotation Z of the turntable 52 using the images 62.

The first step 110 of the spectacles modeling phase consists ofobtaining images of the real pair of spectacles 4 from a number oforientations (preferably keeping a constant distance between the cameraand the object to be photographed), and under a number of lightingconditions. In this step 110, the lens 4 b must match the lens intendedfor the trying-on phase 500.

The real pair of spectacles 4 is photographed with a camera, at highresolution (typically a higher resolution than 1000×1000) in nine (moregenerally V) different orientations and in N light configurationsshowing the transmission and reflection of the spectacle lens 4 b.

These nine (V) orientations are called reference orientations and in therest of the description are designated by Orientation^(i). These Vreference orientations Orientation^(i) are selected by discretizing aspectrum of orientations corresponding to possible orientations whenspectacles are tried on. V*N high-resolution images of the real pair ofspectacles 4 are thus obtained, designated Image-spectacles^(i,j)(1≦i≦V, 1≦j≦N).

In the present example, the number V of reference orientationsOrientation^(i) is equal to nine, i.e. a relatively small number oforientations from which to derive a 3D geometry of the model. However,it is clear that other numbers of orientations may be envisaged with nosubstantial change to the method according to the invention.

If an orthogonal reference space with axes x, y, z is defined, where they-axis corresponds to the vertical axis, 4/ to the angle of rotationaround the x-axis, φ to the angle of rotation around the y-axis, thenine positions Orientation^(i) selected here (defined by the pair φ, ψ)are such that the angle ψ takes the respective values −16°, 0° or 16°,the angle φ takes the respective values −16°, 0° or 16°.

FIG. 4 represents a real pair of spectacles 4 and the nine orientationsOrientation^(i) of the shots.

In the present implementation example of the method, two lightconfigurations are chosen, i.e. N=2. By choosing nine camera positions(corresponding to the reference orientations Orientation^(i)), i.e. V=9,and two light configurations, N=2, eighteen high-resolution imagesImage-spectacles^(i,j) representing a real pair of spectacles 4 areobtained; these eighteen high-resolution images Image-spectacles^(i,j)correspond to the nine orientations Orientation^(i) in the two lightconfigurations.

The first light configuration respects the colors and materials of thereal pair of spectacles 4. Neutral conditions of luminosity are used forthis first light configuration. The nine (and more generally V) imagesImage-spectacles^(i,l) created in this light configuration allow themaximum transmission of light through the lenses 4 b to be revealed(there is no reflection on the lens and the spectacle arms can be seenthrough the lenses). They are called high-resolution transmission imagesand in the rest of the description are designated by Transmission^(i);the exponent i is used to characterize the i^(th) view, where i variesfrom 1 to V.

The second light configuration highlights the special geometric featuresof the real pair of spectacles 4, such as, for example, the chamfers.This second light configuration is taken in conditions of intensereflection.

The high-resolution images Image-spectacles^(i,2) obtained in thissecond light configuration reveal the physical reflection properties ofthe lens 4 b (the arms are not seen behind the lenses, but reflectionsof the environment on the lens are; transmission is minimal). The nine(or V) high-resolution images of the real pair of spectacles 4, createdin this second light configuration are called high-resolution reflectionimages and in the rest of the description are designated byReflection^(i); the exponent i is used to characterize the i^(th) view,where i varies from 1 to V.

According to the method just described, the set of high-resolutionimages Image-spectacles^(i,j) of real pairs of spectacles comprises, bydefinition, both the high-resolution transmission imagesTransmission^(i) and the high-resolution reflection imagesReflection^(i). Obtaining the set of high-resolution imagesImage-spectacles^(i,j) by this step 110 is illustrated in FIG. 5.

Step 120: Generating Overlays of Spectacles

The second step 120 of spectacles modeling phase 100 consists ofgenerating overlays for each of the nine reference orientationsOrientation^(i). A schematic of this second step 120 is shown in FIG. 6.It is understood that an overlay is defined here in the sense known tothe expert in the field of image processing. An overlay is a rasterimage with the same dimensions as the image from which it is derived.

For each of the nine (and more generally V) reference orientationsOrientation^(i), the high-resolution reflection image Reflection^(i) istaken. A binary image is then generated with the same resolution as thehigh-resolution reflection image of the reference orientations. Thisbinary image actually shows the “outline” shape of the lenses 4 b of thereal pair of spectacles 4. This binary image is called a lens silhouetteand is designated Lens^(i) _(binary).

Extraction of the shape of the lenses needed generate the lenssilhouette is performed by an active contours algorithm (e.g. of a typeknown to those skilled in the art under the name “2D snake”) based onthe assumption that the frame 4 a and the lenses 4 b have differenttransparencies. The principle of this algorithm, known per se, is todeform a curve having several deformation constraints. At the end of thedeformation, the optimized curve follows the shape of the lens 4 b.

The curve to be deformed is defined as a set of 2D points placed on aline. The k^(th) point of the curve associated with the coordinate xk inthe high-resolution reflection image Reflection^(i) associated to acurrent reference orientation, has an energy E(k). This energy E(k) isthe sum of an internal energy E_(internal)(k) and an external energyE_(external)(k). The external energy E_(external)(k) depends on thehigh-resolution reflection image Reflection^(i) associated to a currentreference orientation, whereas the internal energy E_(internal)(k)depends on the shape of the curve. This therefore gives E_(external)(k)∇(xk), where ∇ is the gradient of the high-resolution reflection imageReflection^(i) associated to a current reference orientation. Theinternal energy E_(internal)(k) is the sum of an energy referred to asthe “balloon” energy E_(balloon)(k), and a curvature energyE_(curvature)(k) This therefore givesE_(internal)(k)=E_(balloon)(k)+E_(curvature)(k)

The balloon energies E_(balloon)(k) and the curvature energiesE_(curvature)(k) are calculated using standard formulas in the field ofactive contour methods, such as the method known as the Snake method.

In this lens silhouette Lens^(i) _(binary), the value of the pixel isequal to one if the pixel represents the lenses 4 b, and zero if not(which, in other words, in effect forms an outline image).

It is understood that it is also possible to use gray scales (valuesbetween 0 and 1) instead of binary levels (values equal to 0 or 1) toproduce such a lens overlay (for example by creating a gradualtransition between the values 0 and 1 either side of the optimized curveobtained by the active contours method described above).

A lens overlay, designated Lens^(i) _(overlay), is then generated foreach of the nine (V) reference orientations by copying, for each pixelwith a value equal to one in the lens silhouette Lens^(i) _(binary), theinformation contained in the high-resolution reflection imageReflection^(i) and assigning zero to the other pixels. The exponent i ofvariables Lens^(i) _(binary) and Lens^(i) _(overlay) varies from 1 to V,where V is the number of reference orientations.

This lens overlay Lens^(i) _(overlay) is, to some extent, ahigh-definition cropped image of the lens using, for cropping theoriginal high-definition image, the lens silhouette Lens^(i) _(binary)(outline shape) created previously.

Designating the term to term matrix product operator by

, this gives:

Lens^(i) _(overlay)=Lens^(i) _(binary)

Reflection^(i)  (Eq 1)

Thus, for a pixel with position x, y

Lens^(i) _(overlay)(x,y)=Lens^(i) _(binary)(x,y)×Reflection^(i)(x,y)

For each of the reference orientations, the associated high-resolutionreflection image Reflection^(i) is chosen, and then, for each of them, abinary background image Background^(i) _(binary) is then generated byautomatically extracting the background, using a standard imagebackground extraction algorithm. A binary image, called binary frameoverlay Frame^(i) _(binary), is then generated for each of the Vreference orientations, by deducting from a neutral image the outlineimage of the lenses and the outline image of the background, i.e. inmore mathematical terms, by applying the formula:

Frame^(i) _(binary)=1−(Lens^(i) _(binary)+Background^(i) _(binary))  (Eq2)

An overlay, referred to as the texture overlay of the frame behind thelens Frame^(i) _(behind) _(—) _(lens), is then generated of the textureof the frame corresponding to the portion of the frame located behindthe lenses 4 b (for example, a portion of the arms may be visible behindthe lens 4 b depending on the orientation) for each of the nine (V)reference orientations, by copying, for each pixel with a value equal toone in the binary lens overlay Lens^(i) _(binary), the informationcontained in the high-resolution transmission image Transmission^(i),and assigning zero to the other pixels.

This gives: Frame^(i) _(behind) _(—) _(lens)=Lens^(i)_(binary)(x,y)×Transmission^(i)  (Eq 3)

Thus, for a pixel with position x, y:

Frame^(i) _(behind) _(—) _(lens)(x,y)=Lens^(i)_(binary)(x,y)×Transmission^(i)(x,y)

Similarly an overlay, referred to as the texture overlay of the frameoutside the lens Frame^(i) _(exterior) _(—) _(lens) is generated foreach of the nine (V) reference orientations by copying, for each pixelwith a value equal to one in the binary frame overlay Frame^(i)_(binary), the information contained in the high-resolution reflectionimage Reflection^(i) and assigning zero to the other pixels.

The exponent i of variables Frame^(i) _(binary), Background^(i)_(binary), Frame^(i) _(exterior) _(—) _(lens) and Frame^(i) _(behind)_(—) _(lens) varies from 1 to V, where V is the number of referenceorientations Orientation^(i).

This gives: Frame^(i) _(exterior) _(—) _(lens)=Frame^(i) _(binary)

Reflection^(i)  (Eq 4)

A texture overlay of the frame Frame^(i) is defined as the sum of thetexture overlay of the frame behind the lens Frame^(i) _(behind) _(—)_(lens) and the texture overlay of the frame outside the lens Frame^(i)_(exterior) _(—) _(lens).

This gives: Frame^(i)=Frame^(i) _(behind) _(—) _(lens)=Frame^(i)_(exterior) _(—) _(lens)  (Eq 5)

Step 130: Geometric model The third step 130, of the spectacles modelingphase 100 consists of obtaining a simplified geometric model 6 of a realpair of spectacles 4. A real pair of spectacles 4 comprises a frame 4 aand lenses 4 b (the notion of lenses 4 b comprises the two lensesmounted in the frame 4 a). The real pair of spectacles 4 is representedin FIG. 1 a.

This step 130 does not involve the reflection characteristics of thelenses 4 b mounted in the frame 4 a; the real pair of spectacles 4 maybe replaced by a pair of spectacles comprising the same frame 4 a withany lenses 4 b having the same thickness and curvature.

This simplified geometric model 6, can be obtained:

-   -   either by extracting its definition (radius of curvature of the        frame, dimensions of the frame) from a database DB_(models) _(—)        _(spectacles) of geometric models associated to pairs of        spectacles.    -   or, according to the preferred approach, by constructing the        simplified geometric model 6 using a construction procedure. The        new geometric model 6, thus created, is then stored in a        database of models DB_(models) _(—) _(spectacles).

There are several possible ways to construct a geometric model suitablefor the rendering method described in step 120. One possible method isto generate a dense 3 d mesh that faithfully describes the shape of thepair and is extracted either by automatic reconstruction methods [C.Hernández, F. Schmitt and R. Cipolla, Silhouette Coherence for CameraCalibration under Circular Motion, IEEE Transactions on Pattern Analysisand Machine Intelligence, vol. 29, no. 2, pp. 343-349, February, 2007]or by exploiting existing 3D models from manual modeling by CAD(Computer Aided Design) systems. A second method consists of modelingthe real pair of spectacles 4 by a 3D active contour linked to a surfacemesh. An optimization algorithm deforms the model so that theprojections of its silhouette in each of the views best match thesilhouettes detected in the images (using a procedure as described).

The real pair of spectacles 4 is modeled by a surface mesh that is denseor has a low number of facets (traditionally known by the name “lowpolygon number” or “LowPoly”). This last method is. The initial shape isused to introduce one a priori with a weak shape; it can be generic orchosen from a database of models according to the pair to bereconstructed. In what follows, the case of a simplified geometric model(i.e. a “low polygons” type) will be described.

The mesh comprises N summits, designated V_(i). The mesh has the shapeof a triangle strip, as shown in FIG. 1 b. Furthermore it is assumedthat the number of summits on the upper contour of the mesh is equal tothe number of summits on the lower contour of the mesh, and that thesampling of these two contours is similar. Thus, an “opposite” summit,V_(i) ⁺ can be defined for each summit V_(i).

Regardless of the actual topology of the mesh, the neighborhood N_(i) ofthe summit V_(i) is described by N_(i)={V_(i+1); V_(i−1); V_(i) ⁺}

The summits V_(i+1) and V_(i−1) are the neighbors of V_(i) along thecontour of the mesh. The summit V_(i) ⁺ corresponds to the summitopposite to V_(i), as defined earlier. This neighborhood also allows twotriangles T_(i) ¹ and T_(i) ² to be constructed (see FIG. 1 c). Let n¹and n² be their respective normals. The normal to the surface in segmentV_(i) ⁺ V_(i) (which is a topological peak or not) is defined by

$\begin{matrix}{n = \frac{n^{1} + n^{2}}{{n^{1} + n^{2}}}} & \left( {{Eq}\mspace{14mu} 6} \right)\end{matrix}$

To develop the active contour to the image data, an energy is associatedto the current 3D model: the closer the projected silhouettes of themodel are to the contours in the images, the lower this energy is. Eachsummit is then displaced iteratively so as to minimize this energy untilconvergence (i.e. until the energy is no longer reduced by adisplacement). In addition, one seeks to obtain a smooth model, whichleads us to define at each summit an internal energy not dependent onimages. The energy associated to the summit V_(i) is given by:

E _(i)=λ_(d,i)+λ_(r) E _(r,i)+λ_(c) E _(c,i)+λ_(o) E _(o,i)  (Eq 7)

The term E_(d,i) is the linking term to the image data, i.e. to thecontours calculated in the different views. The three other terms aresmoothing terms, which do not depend on images.

The term E_(r,i) is a repulsion term that tends to distribute thesummits uniformly.

The term E_(c,i) is a curvature term that tends to make the surfacesmooth.

Finally the term E_(o,i) is an obliquity term aimed at minimizing thegap in the (x; y) plane between Vi and V_(i) ⁺

The weights λ_(d), λ_(r), λ_(c), λ_(o) are common to all the summits andin general λ_(d), λ_(r), λ_(c), λ_(o).

The linking term to data E_(d,i) characterizes the proximity of thesilhouette of the current active contour with the contours detected inthe images (by an active contour procedure as described in step 120above). In the acquisition process, an automatic cropping phase, of atype known per se (“difference matting”), provides an opacity map foreach view.

The contours are obtained by thresholding the gradient of this opacitymap. The contour information is propagated to the entire image bycalculating, for each view k, a map of distances to the contours,designated D_(k). The projection model of the 3D model in the images isa model of pinhole camera, of a type known per se, defined by thefollowing elements:

-   -   a matrix K_(k) (3×3 matrix) containing the camera's internal        parameters,    -   a matrix E_(k)=[R_(k)|t_(k)](3×4 matrix) describing the switch        from the world reference space (as presented in FIG. 1 b) to the        camera reference space of view k.

${\psi_{k}\left( {x;y;z} \right)} = \left( {\frac{u}{w},\frac{v}{w}} \right)^{T}$

designates the projection of 3D point (x, y, z)^(T) in view k. It isobtained by

$\begin{matrix}{\begin{pmatrix}u \\v \\w\end{pmatrix} = {K_{k}{E_{k}\begin{pmatrix}x \\y \\z \\1\end{pmatrix}}}} & \left( {{Eq}\mspace{14mu} 8} \right)\end{matrix}$

The linking energy to the data is thus expressed by:

$\begin{matrix}{E_{d,i} = {\frac{1}{S}{\sum\limits_{k \in S}\left( {D_{k}\left( {\Psi_{k}\left( V_{i} \right)} \right)} \right)^{2}}}} & \left( {{Eq}\mspace{14mu} 9} \right)\end{matrix}$

where S and the set of views in which the summit V_(i) is visible and|S| its cardinal.

The repulsion term E_(r,i) tends to minimize the difference in length oftwo peaks of the contour joining at V_(i). It is expressed by:

$\begin{matrix}{E_{r,i} = \left( \frac{{{V_{i - 1} - V_{i}}} - {{V_{i + 1} - V_{i}}}}{{{V_{i - 1} - V_{i}}} + {{V_{i + 1} - V_{i}}}} \right)^{2}} & \left( {{Eq}\mspace{14mu} 10} \right)\end{matrix}$

The curvature term E_(c,i) tends to reduce the curvature perpendicularto segment V_(i) ⁺ V_(i)

The corresponding energy is expressed by

E _(c,i)=(1−n ^(1T) n ²)²  (Eq 11)

where n¹ and n² are the normals defined above.

The obliquity term E_(o,i) tends to preserve the vertical correspondencebetween the points of the upper contour and the points of the lowercontour. For this, it is assumed that the orientation of the model ofthe spectacles is as in FIG. 1, namely that the z axis is the axisperpendicular to the natural plane of the pair “placed on the table”.

This thus gives E_(o,i)=(d_(i) ^(T) a)²  (Eq 12)

where d_(i) designates segment V_(i) ⁺V_(i)

The resolution is done by scanning each summit V_(i) of the meshiteratively and one seeks to minimize the associated energy functionE_(i). This is a nonlinear function, therefore a Newton type ofiterative minimization method is used. The development, limited to thesecond order of the energy function, for a small displacement δ_(i) ofthe summit, is expressed by:

E _(i)(V _(i)+δ_(i))≈(V _(i))+∇_(Ei) ^(T)δ_(i)+δ_(i) ^(T) H_(Ei)δ_(i)  (Eq 13)

where ∇_(Ei) is the gradient of E_(i) and H_(Ei) is its Hessian matrix(both evaluated in V_(i)).

The initial non-linear minimization problem is replaced by a successionof linear problems.

Let f (δ_(i))=E_(i)(V_(i))+∇_(Ei) ^(T)δ_(i)+δ_(i) ^(T)H_(Ei)δ_(i) andone seeks the minimum {circumflex over (δ)}_(i) of f relative to δ_(i).

It satisfies the condition: f′({circumflex over (δ)}_(i))=0, i.e. ∇_(Ei)^(T)+H_(Ei){circumflex over (δ)}_(i)=0

At each iteration, the summit V_(i) ^(k−1) is displaced in the direction{circumflex over (δ)}_(i) ^(k)

V _(i) ^(k) =V _(i) ^(k−1)+δ^(k){circumflex over (δ)}_(i) ^(k)  (Eq 14)

The length of step λ^(k) is either optimized (a standard method referredto as “line-search”), or determined beforehand and left constantthroughout the procedure.

The iterative procedure described above is stopped when the step isnormally below a threshold, when more than k_(max) iterations have beenperformed, or when the energy E_(i) does not reduce sufficiently fromone iteration to the next.

In a variant to this construction procedure, 3D modeling software isused to model the geometry of the real pair of spectacles 4.

In another variant of this construction procedure, a model of thedatabase of models DB_(models) _(—) _(spectacles) is used and it isadapted manually.

The simplified geometric model 6 is formed of a number N of polygons andtheir normals, taking as the orientation of these normals the exteriorof the envelop convex to the real pair of spectacles 1. In thisnon-limiting example the number N is a value close to twenty.

FIG. 1 d represents a simplified model for a pair of wraparound sportsspectacles. In the remainder of the description, these polygons of thesimplified geometric model 6, are called the surfaces of the modeledpair of spectacles designated by surface_(j). The normal to a surface ofthe modeled pair of spectacles surface_(j) is designated by {right arrowover (n)}_(j); j is a numbering index of the surfaces surface_(j) whichvaries from 1 to N.

A schematic of step 130 is shown in FIG. 3.

Step 140: Creating a Shadow Map

In this step a shadow map, designated Visibility^(i), is created foreach of the reference orientations Orientation^(i). The goal is tocalculate the shadow produced by the pair of spectacles on a face,modeled here by an average face 20, a 3D model constructed in the formof a mesh of polygons (see FIG. 7 a).

The modeling of the face in question corresponds to an average face 20,which makes it possible to calculate a shadow suitable for any person.The method calculates the light occlusion produced by the pair ofspectacles on each area of the average face 20. The technique envisagedallows very faithful shadows to be calculated while requiring only asimplified geometric model 6 of the real pair of spectacles 4. Thisprocedure is applied to calculate the shadow produced by the pair ofspectacles, for each image of said pair of spectacles. The final resultobtained are 9 shadow maps Visibility^(i) corresponding to the 9reference orientations Orientation^(i) used, in this example, during thecreation of the image-based rendering.

For each reference orientation, this shadow map Visibility^(i) iscalculated using the simplified geometric model 6 of the real pair ofspectacles 4 (“low polygons” surface simplified model, see step 130), atextured reference model 9 (superimposition of texture overlays of thepair of spectacles corresponding to a reference orientation) orientedaccording to the reference orientation Orientation^(i), a modeling of anaverage face 20, a modeling of a light source 21 and a modeling 22 of acamera.

The shadow map Visibility^(i) is obtained by calculating the lightocclusion produced by each elementary triangle forming the simplifiedgeometric model 6 of the real pair of spectacles 4, on each area of theaverage face 20, when everything is lit by the light source 21. Thelight source 21 is modeled by a set of point sources emitting in alldirections, located at regular intervals in a rectangle, for example asa 3×3 matrix of point sources.

The modeling 22 of a camera is standard modeling of a type known aspinhole, i.e. modeling without a lens and with a very small and simpleopening. The shadow map Visibility^(i) obtained is an image comprisingvalues between 0 and 1.

The coordinates (X,Y) of the 2D projection of a vertex (x,y,z) of the 3Dscene is expressed as follows:

$\begin{matrix}{{X = {{u\; 0} + {f \times \frac{x}{z}}}},{Y = {{v\; 0} + {f \times \frac{y}{z}}}}} & \left( {{Eq}\mspace{14mu} 15} \right)\end{matrix}$

in which the parameters u₀, v₀, f characterize the camera.

Let K designate the operator that, at a vertex V(x,y,z), associates itsprojection P(X,Y) in the image. A set of 3D points {V} corresponds to apixel P with coordinates (X,Z) such that K(V)=P.

The set of these 3D points forms a radius. Subsequently, when referenceis made to a 3D radius associated with a pixel, the 3D radiuscorresponds to the set of 3D points projected on the pixel.

To calculate the shadow, for each pixel P with coordinate (i,j) in theshadow image Visibility^(i), the value O(i,j) of the shadow image iscalculated. For this, the 3D vertex V of the face that corresponds tothe projection P is calculated. This term V is defined as theintersection of the 3D radius defined by the pixel and the 3D model ofthe face 20 (see FIG. 7 b).

Then, the light occlusion produced by the pair of spectacles on thisvertex is calculated. To do this, the light occlusion produced by eachtriangle of the low-resolution geometric model 6 is calculated.

Let A(m), B(m), C(m) designate the three summits of the mth triangle ofthe low-resolution geometric model 6. For each light point source Sn,the intersection tn of the light ray passing through Vis calculated.

Tn is the 2D projection of vertex tn on the texture image (texturedreference model 9 of the pair of spectacles). The transparency of thetexture is known from step (120) of cropping on differences, therefore,the pixel Tn has a transparency, designated by a(Tn).

Finally, the value of pixel O(i,j) of the shadow image is expressed asfollows:

$\begin{matrix}{{O\left( {i,j} \right)} = {{Coefficient} \times {\sum\limits_{m = 1}^{NTriangles}{\sum\limits_{n = 1}^{NSources}{a\left( {T\left( {m,n} \right)} \right)}}}}} & \left( {{Eq}\mspace{14mu} 16} \right)\end{matrix}$

The Coefficient term allows the opacity of the shadow Visibility^(i) tobe adjusted according to the visual rendering wanted.

The data obtained in phase 100 are stored in a spectacles databaseDB_(models) _(—) _(spectacles) that contains, for each pair ofspectacles modeled, the simplified geometric model 6 of this real pairof spectacles 4, the lens overlays Lens^(i) _(overlay), the overlays ofthe frame behind the lens Frame^(i) _(behind) _(—) _(lens) and theoverlays of the frame outside the lens Frame^(i) _(exterior) _(—)_(lens), for each of the V reference orientations.

In addition, data specific to the lenses 4 b of the real pair ofspectacles 4 are added to the previously mentioned data in thespectacles database DB_(models) _(—) _(spectacles), such as itscoefficient of opacity α, known by the manufacturer, and possiblysupplied for each reference orientation.

Phase 200 of Creating a Database of Models of Eyes DB_(models) _(—)_(eyes)

The second phase 200 makes it possible to create a database of models ofeyes, DB_(models) _(—) _(eyes). To simplify its description, it issubdivided into ten steps (210, 220, 230 to 236 and 240). The databaseof models of eyes, DB_(models) _(—) _(eyes), thus obtained is used, inthe trying-on phase 500, to characterize the eyes of a personphotographed.

This eyes database DB_(models) _(—) _(eyes) can be created, for example,from at least two thousand photographs of faces, referred to as learningphotographs App_(eyes) ^(k) (1≦k≦2000). These learning photographs areadvantageously, but not obligatorily, the same size as the images ofmodels of spectacles and of the face of the user in the trying-onmethod.

Step 210. When this eyes database DB_(models) _(—) _(eyes) is created,first of all a reference face 7 shape is defined by setting a referenceinterpupillary distance di₀, by centering the interpupillary segment onthe center of the image and orienting the interpupillary segmentparallel to the horizontal axis of the image (face not tilted). Thereference face 7 is therefore centered on the image, with the faceorientation and magnification depending on the reference interpupillarydistance di₀.

Step 220. In a second step a correlation threshold threshold is defined.

Then, for each k^(th) learning photograph App_(eyes) ^(k) not yetprocessed, steps 230 to 236 are applied.

Step 230—The precise position of characteristic points (corners of theeyes) are determined, manually in this example, i.e. the position of theexterior point B_(l) ^(k), B_(r) ^(k) of each eye (left and rightrespectively with these notations) and the position of the interiorpoint A_(l) ^(k), A_(r) ^(k), as defined in FIG. 8. Each position isdetermined by its two coordinates within the image.

The respective geometric centers G_(l) ^(k), G_(r) ^(k) of these eyesare determined, calculated as the barycenter of the exterior point B^(k)of the corresponding eye and the interior point A^(k) of this eye, andthe interpupillary distance di^(k) is calculated.

Step 231—This k^(th) learning photograph App_(eyes) ^(k) is transformedinto a gray-scale image App_(eyes-gray) ^(k), by an algorithm known perse, and the gray-scale image is normalized by applying a similarityS^(k)(tx, ty, s, Θ) so as to establish the orientation (front view),scale (reference interpupillary distance di₀) of the reference face 7.

This similarity S^(k)(tx, ty, s, Θ) is determined as the mathematicaloperation to be applied to the pixels of the learning photographApp_(eyes) ^(k) to center the face (center of eyes equal to the centerof the photograph), orientation of face and magnification depending onthe reference interpupillary distance di₀. The terms tx and ty designatethe translations to be applied on the two axes of the image so as toestablish the centering of the reference face 7. Similarly, the term sdesignates the magnification factor to be applied to this image, and theterm Θ designates the rotation to be applied to the image so as toestablish the orientation of the reference face 7.

A k^(th) gray-scale normalized learning photograph App_(eyes) _(—)_(gray) _(—) _(norm) ^(k) is thus obtained. The interpupillary distanceis equal to the reference interpupillary distance di₀. Theinterpupillary segment is centered on the center of the k^(th)gray-scale normalized learning photograph App_(eyes) _(—) _(gray) _(—)_(norm) ^(k). The interpupillary segment is parallel to the horizontalaxis of the gray-scale normalized learning photograph App_(eyes) _(—)_(gray) _(—) _(norm) ^(k).

Step 232—A window, rectangular in this example, with a fixed size (widthw and height h) is defined for each of the eyes, in the k^(th)gray-scale normalized learning photograph App_(eyes) _(—) _(gray) _(—)_(norm) ^(k). These two windows are called the left patch P_(l) ^(k) andright patch P_(r) ^(k) in the remainder of this description, accordingto a standard usage in this field. For simplicity, the term patch P willbe used to denote either one of these patches P_(l) ^(k), P_(r) ^(k).

Each patch P is a sub-raster image extracted from an initial rasterimage of a face. It is clear that, in a variant, a shape other thanrectangular may be used for the patch, for example polygonal, ellipticalor circular.

The position of the patch P corresponding to an eye (left, rightrespectively), is defined by the fixed distance Δ between the exteriorpoint of the eye B and the edge of the patch P closest to this exteriorpoint of the eye B (see FIG. 7).

This fixed distance Δ is chosen so that no texture exterior to the faceis included in the patch P. The width w and height h of patches P_(l)^(k), P_(r) ^(k) are constant and predefined, so patch P contains theeye corresponding to this patch P in full, and contains no texture thatis external to the face, irrespective of the learning photographApp_(eyes) ^(k).

Step 233—For each of the two patches P_(l) ^(k), P_(r) ^(k) associatedto the k^(th) gray-scale normalized learning photograph APP_(eyes) _(—)_(gray) _(—) _(norm) ^(k) (each corresponding to one eye), thegray-scales are normalized.

To do this, a texture column-vector T, called the original texturecolumn-vector, is defined, comprised of the gray-scales for patch P, inthis example stored in row order the size of the texture column-vector Tis equal to the number of lines (h) multiplied by the number of columns(I) and a column-vector I with a unit value is defined, the same size asthe texture column-vector T.

The mathematical operation therefore consists of calculating the mean ofthe gray-scales of patch P, mean designated μ_(T), of normalizing thestandard deviation of these gray-scales, designated σ_(T), and ofapplying the formula:

$\begin{matrix}{{T\; 0} = \frac{\left( {T - {\mu_{T}I}} \right)}{\sigma_{T}}} & \left( {{Eq}\mspace{14mu} 17} \right)\end{matrix}$

where T0 is the normalized texture column-vector (gray-scale) and T theoriginal texture column-vector.

Step 234—This step 234 is only performed for the first learningphotograph App_(eyes) ¹. The eyes database DB_(models) _(—) _(eyes) istherefore empty.

For the first learning photograph App_(eyes) ¹ processed, each of thepatches P_(l) ¹, P_(r) ¹ is added to the eyes database DB_(models) _(—)_(eyes); with the following data stored:

-   -   the normalized texture column-vector T0 _(l) ¹, T0 _(r) ¹        corresponding to a patch P_(l) ¹, P_(r) ¹,    -   the precise position of the normalized characteristic points, by        applying the similarity S¹(tx, ty, s, Θ) to the precise        positions of characteristic points identified beforehand in the        learning photograph App_(eyes) ¹,    -   the similarity S¹(tx, ty, s, Θ),    -   and other useful information: morphology, brightness etc.,        then one goes to step 230 for processing the second learning        photograph App_(eyes) ² and the following ones App_(eyes) ^(k).

Patches P_(l) ¹, P_(r) ¹ stored in the eyes database DB_(models) _(—)_(eyes) in this step 234 and in step 236 are called descriptor patches.

Step 235—For each of the patches P associated to the k^(th) gray-scalenormalized learning photograph App_(eyes) _(—) _(gray) _(—) _(norm) ^(k)(each corresponding to one eye), the corresponding normalized texturecolumn-vector T0 is correlated with each of the normalized texturecolumn-vectors T0 of the corresponding descriptor patches.

In this non-limiting example a correlation measurement Z_(ncc) is used,defined for example by

Z _(ncc)(T0,T0_(i))=^(t) T0*T0_(i)  (Eq 18)

(where ^(t)T0 designates the transposed vector of the normalized texturecolumn-vector T0). As the sizing of patches P_(l) ^(k), P_(r) ^(k) isconstant, the normalized texture column-vectors T0, T0 _(i) all have thesame size.

Step 236—For each of the patches P_(l) ^(k), P_(r) ^(k), thiscorrelation measurement Z_(ncc) is compared against the previouslydefined correlation threshold threshold. If correlation Z_(ncc) is belowthe threshold, i.e. Z_(ncc) (T0 ^(k), T0 _(i))<threshold, patch P isadded to the eyes database DB_(models) _(—) _(eyes), with the followingdata stored:

-   -   the normalized texture column-vector T0 ^(k),    -   the precise position of the normalized characteristic points, by        applying the similarity S^(k)(tx, ty, s, Θ) to the precise        positions of characteristic points identified beforehand in the        learning photograph App_(eyes) ^(k),    -   the similarity S^(k)(tx, ty, s, Θ)    -   and other useful information: morphology, brightness etc.

A new learning photograph App_(eyes) ^(k+1) can now be processed byreturning to step 230.

Step 240—A statistical operation is performed on all the similaritiesS^(k)(tx, ty, s, Θ) stored in the database DB_(models) _(—) _(eyes).

First of all, the mean value of the translation tx and the mean value ofthe translation ty are calculated; these values will be stored in atwo-dimensional vector {right arrow over (μ)}.

Secondly, the standard deviation σ is calculated for position parameterstx, ty relative to their mean, characterized by {right arrow over (μ)}.

In a variant, the precise positions of the characteristic points of theeyes, (these precise positions here are non-normalized) determinedbeforehand in the k^(th) learning photograph App_(eyes) ^(k). Thesimilarity S^(k)(tx, ty, s, Θ) or the values of all the parametersallowing these precise positions to be re-calculated, are also stored.

Phase 300: Method of Searching for Criteria for Recognizing a Face in aPhoto.

The purpose of phase 300 is to detect the possible presence of a face ina photo. A boosting algorithm is used, of a type known per se and, forexample, described by P. Viola and L. Jones “Rapid object detectionusing a boosted cascade of features” and improved by R. Lienhart “adetector tree of boosted classifiers for real-time object detectiontracking”.

It is noted that, in the field of automatic learning, the termclassifier refers to a family of statistical classification algorithms.In this definition, a classifier groups together in the same classelements presenting similar properties.

Strong classifier refers to a very precise classifier (low error rate),as opposed to weak classifiers, which are not very precise (slightlybetter than a random classification).

Without going into details, which are outside the framework of thisinvention, the principle of boosting algorithms is to use a sufficientnumber of weak classifiers to make a strong classifier, achieving adesired classification success rate, emerge by selection or combination.

Several boosting algorithms are known. In this example the boostingalgorithm known under the brand name “AdaBoost” (Freund and Schapire1995) is used to create several strong classifiers (e.g. twenty) thatwill be organized in a cascade, in a manner known per se.

In the case of searching for a face in a photo, if a strong classifierthinks it has detected a face at the analysis level at which it operateswith its set of weak classifiers, then it passes the image to the nextstrong classifier, which is more accurate, less robust but freed fromsome uncertainties due to the previous strong classifier.

In order to obtain a cascade with good classification properties in anuncontrolled environment (variable lighting conditions, variablelocations, substantially variable faces to be detected), it is necessaryto establish a face learning database DBA_(faces).

This face learning database DBA_(faces) consists of a set of imagesreferred to as positive examples of faces Face_(positive) (type ofexample that one wants to detect) and a set of images referred to asnegative examples of faces Face_(negative) (type of example that onedoes not want to detect). These images are advantageously, but notobligatorily, the same size as the images of models of spectacles and ofthe face of the user in the trying-on method.

To generate the set of images referred to as the positive examples offaces Face_(positive), first of all reference face imagesFace_(reference) are selected such that:

-   -   these faces are the same size (e.g. one can require the        interpupillary distance in the image to be equal to the        reference interpupillary distance di₀),    -   the segment between the centers of the two eyes is horizontal        and vertically centered on the image, and    -   the orientation of this face is either a front view or slightly        in profile, between −45° and 45°.

The set of these reference face images Face_(reference) must compriseseveral lighting conditions.

Secondly, based on these reference face images Face_(reference), othermodified images Face_(modified) are constructed by applying variationsin scale, rotation and translation in bounds determined by a normaltrying on of a pair of spectacles (e.g. unnecessary to create aninverted face).

The set of images referred to as the positive examples of facesFace_(positive) consists of reference face images Face_(referenee) andmodified images Face_(modified) based on these reference face imagesFace_(reference). In this example, the number of examples referred to aspositive examples of faces Face_(positive) is greater than or equal tofive thousand.

The set of images of negative examples of faces Face_(negative) consistsof images that cannot be included in the images referred to as positiveexamples of faces Fac_(positive).

These are, therefore, images that do not represent faces, or imagesrepresenting parts of faces, or faces that have undergone aberrantvariations. In this example, a group of pertinent images is taken foreach level of the cascade of strong classifiers. For example, fivethousand images of negative examples of faces Face_(negative) areselected for each level of cascade. If, as in this example, one chosesto use twenty levels in the cascade, this gives one hundred thousandimages of negative examples of faces Face_(negative) in the facelearning database DBA_(faces).

Phase 300 uses this face learning database DBA_(faces) train the firstboosting to algorithm AD1, designed to be used in step 510 of phase 500.

Phase 400: Method of Searching for Criteria for RecognizingCharacteristic Points in a Face

The purpose of phase 400 is to provide a method for detecting theposition of the eyes in a face in a photo. In this example, the positionof the eyes is detected with a second, Adaboost-type, detectionalgorithm AD2, trained with an eyes learning database DBA_(eyes)described below.

The eyes learning database DBA_(eyes) consists of a set of positiveexamples of eyes Eyes_(positive) (positive examples of eyes are examplesof what one wants to detect) and a set of negative examples of eyesEyes_(negative) (negative examples of eyes are examples of what one doesnot want to detect).

To generate the set of images referred to as positive examples of eyesEyes_(positive), first of all reference eye images Eyes_(reference), areselected such that the eyes are of the same size, straight (alignedhorizontally) and centered, under different lighting conditions and indifferent states (closed, open, half-closed, etc.),

Secondly, based on these reference eye images Eyes_(referenee), othermodified eye images Eyes_(modified) are constructed by applyingvariations in scale, rotation and translation in limited bounds.

The set of images referred to as the positive examples of eyesEyes_(positive) will therefore consist of reference eye imagesEyes_(reference) and modified eye images Eyes_(modified) based on thesereference eye images Eyes_(reference). In this example, the number ofexamples referred to as positive examples of eyes Eyes_(positive) isgreater than or equal to five thousand.

The set of images of negative examples of eyes Eyes_(negative) must beconstituted of images of parts of the face that are not eyes (nose,mouth, cheek, forehead, etc.) or of partial eyes (bits of the eye).

To increase the number and pertinence of the negative examples of eyesEyes_(negative), additional negative images are constructed based onreference eye images Eyes_(reference) by applying sufficiently greatvariations in scale, rotation and translation so that these images thuscreated are not interesting in the context of images of positiveexamples of eyes Eyes_(positive).

A group of pertinent images is selected for each level of the cascade ofstrong classifiers. For example, five thousand images of negativeexamples of eyes Eyes_(negative) can be selected for each level ofcascade. If there are twenty levels in the cascade, this gives onehundred thousand images of negative examples of eyes Eyes_(negative) inthe eyes learning database DBA_(eyes).

Phase 400 may use this eyes learning database DBA_(eyes) to train asecond boosting algorithm AD2, which is used in a variant of the methodinvolving a step 520.

Phase 500 of Trying on Virtual Spectacles

In phase 500, trying on virtual spectacles, the method of generating afinal image 5 from the original photo 1 is divided into seven steps:

-   -   a step 510 of detecting the face 2 of the subject in an original        photo 1.    -   possibly a step 520 of the preliminary determination of the        position of characteristic points of the subject in the original        photo 1.    -   a step 530 of determining the position of characteristic points        of the subject in the original photo 1.    -   a step 540 of determining the 3D orientation of the face 2.    -   a step 550 of selecting the texture to be used for the virtual        pair of spectacles 3 and generating the view of the spectacles        in the 3D/2D position in question.    -   a step 560 of creating a first rendering 28 by establishing a        layered rendering in the correct position consistent with the        position of the face 2 in the original photo 1.    -   a step 570 of obtaining the photorealistic rendering by adding        overlays, referred to as semantic overlays, so as to obtain the        final image 5.

Step 510: In this example step 510 uses the first boosting algorithm AD1trained in phase 300 to determine whether the original photo 1 containsa face 2. If this is the case one goes to step 520, otherwise the useris warned that no face has been detected.

Step 520: its purpose is to detect the position of the eyes in the face2 in the original photo 1. Step 520 here uses the second boostingalgorithm AD2 trained in phase 400.

The position of the eyes, determined in this step 520, is expressed bythe position of characteristic points. This step 520 thus provides afirst approximation, which is refined in the next step 530.

Step 530: it consists of determining a similarity β, to be applied to anoriginal photo 1, to obtain a face, similar to a reference face 7 inmagnification and orientation, and determining the position of theprecise exterior corner A and the precise interior corner B for each eyein the face 2 in the original photo 1.

The position of the eyes, determined in this step 530, is expressed bythe position of characteristic points. As explained above, thesecharacteristic points comprise two points per eye; the first point isdefined by the most innermost possible corner A of the eye (the onenearest the nose), the second point B is the most outermost corner ofthe eye (the one furthest from the nose). The first point, A, is calledthe interior point of the eye, and the second point, B, is called theexterior point of the eye.

This step 530 uses the database of models of eyes DB_(models) _(—)_(eyes). In addition this step 530 provides information characterizingthe offset from center, distance to the camera and 2D orientation of theface 2 in the original photo 1.

This step 530 uses an iterative algorithm that makes it possible torefine the value of the similarity β and the positions of thecharacteristic points.

The parameters of similarity β and the positions of the characteristicpoints are initialized as follows. Step 520 has provided respectively,for each eye, a first approximate exterior point of the eye A₀ and afirst approximate interior point B₀; these points are used forinitializing the characteristic points. The initialization values of thesimilarity β are deduced from them.

The similarity β is defined by a translation tx, ty in two dimensions x,y, a parameter of scale s and a parameter of rotation θ in the imageplane.

This therefore gives

${\beta_{0} = \begin{pmatrix}x_{0} \\y_{0} \\\theta_{0} \\s_{0}\end{pmatrix}},$

the initial value of β.

The different steps of an iteration are as follows:

The characteristic points are used to create the two patches P_(l),P_(r) containing the two eyes. These patches P_(l), P_(r) are created asfollows;

The original photo 1 is transformed into a gray-scale image 8, by analgorithm known per se, and the two patches P_(l), P_(r) are constructedwith the information about the exterior B and interior A points.

The position of a patch P_(l), P_(r) is defined by the fixed distance D,used prior to this in step 232 and following steps, between the exterioredge B of the eye and the edge of the patch closest to this point B. Thesizing of the patch P_(l), P_(r) (width and height) was defined in step232 and following steps. If the patches P_(l), P_(r) are not horizontal(external and interior points of the patch not aligned horizontally), abilinear interpolation of a type known per se, is used to align them.

The information about the texture of each of the two patches P_(l),P_(r) is stored in a vector (T_(l)), then these two vectors arenormalized by subtracting their respective mean and dividing by theirstandard deviation. This gives two normalized vectors, designated T0_(r) and T0 _(l).

The realization of β is considered in terms of probability. Therealizations of the parameters of position tx, ty, orientation Θ, andscale s, are considered to be independent and, in addition, thedistributions of Θ, s are considered to follow a uniform distribution.

Finally, the parameters of position tx, ty are considered to follow aGaussian distribution with mean vector {right arrow over (μ)} (in twodimensions) and standard deviation σ. The probability that β is realizedis designated by p(β). Taking the variables {right arrow over (μ)}, σand

${\overset{\rightarrow}{v} = \begin{pmatrix}x \\y\end{pmatrix}},$

stored in the eyes database DB_(models) _(—) _(eyes), and established instep 240, an optimization criterion is selected as follows:

$\begin{matrix}{{{argmax}_{x,y,\theta,s}\ln \; {p\left( {\beta/D} \right)}} = {{{argmax}\left( {{\ln \; {p\left( {D_{r}/\beta} \right)}},{\ln \; {p\left( {D_{l}/\beta} \right)}}} \right)} - {K\; \frac{{{\overset{\rightarrow}{v} - \overset{\rightarrow}{\mu}}}^{2}}{2\sigma^{2}}}}} & \left( {{Eq}\mspace{14mu} 19} \right)\end{matrix}$

where

D_(r) are random variable data representing the right patch P_(r),consisting of the texture of the right patch,

D_(l) are random variable data representing the left patch P_(l),consisting of the texture of the left patch,

D=D_(r)∪D_(l) are random variable data representing the two patchesP_(l), P_(r). The realizations of D_(r) and D_(l) are considered to beindependent,

p(β/D) is the probability β is realized given D,

K is a constant,

P(D_(r)/β)=max ρ(D_(r)/β, id)

id represents a descriptor patch (patches stored in the eyes databaseDB_(models) _(—) _(eyes)).

The set of descriptor patches in the eyes database DB_(models) _(—)_(eyes) are then scanned. The term ρ represents the correlation Z_(ncc)(between 0 and 1), formulated in step 235 and following steps, betweenpatch P_(r) of the right eye (respectively P_(l) of the left eye) and adescriptor patch transformed according to the similarity β.

The maximum of these correlations Z_(nc) allows the probabilitiesP(D_(r)/β) (respectively p(D_(l)/β) to be calculated.

The regulation term

${- K}\; \frac{{{\overset{\rightarrow}{v} - \overset{\rightarrow}{\mu}}}^{2}}{2\sigma^{2}}$

makes it possible to ensure the physical validity of the proposedsolution.

The optimization criterion defined above (Equation 19), thus makes itpossible to define an optimal similarity β and an optimal patch from thepatch descriptors for each of the two patches P_(l), P_(r) which allowsnew estimates of the position of the exterior corners A and interiorpoint B of each eye, i.e. characteristic points, to be provided.

It is tested whether this new similarity value β is sufficiently farfrom the previous value, e.g. by a difference of ε: if ∥β_(i-1)−β_(i)∥>εan iteration is repeated. In this iteration, β_(i) represents the valueof β found at the end of the current iteration and β_(i-1) is the valueof similarity β found at the end of the previous iteration, i.e. alsothe initial value of similarity β for the current iteration.

The constant K allows the right compromise to be achieved between thecorrelation measurements Zncc and a mean position from which one doesnot want to depart too far.

This constant K is calculated, using the method just described, on a setof test images, different from the images used to create the database,and by varying K.

It is understood that the constant K is chosen so as to minimize thedistance between the characteristic points of the eyes, manuallypositioned on the training images, and those found in step 530.

Step 540: its purpose is to estimate the 3D orientation of the face,i.e. to provide the angle φ and angle ψ of the camera having taken thephoto 1, relative to the principal plane of the face. These angles arecalculated from the precise position 38 of the characteristic pointsdetermined in step 530, by a geometric transformation known per se.

Step 550: this step consists of:

-   -   firstly, 1/ finding the simplified geometric model 6 of the        model of a virtual pair of spectacles 3, stored in the        spectacles database DB_(models) _(—) _(spectacles), and, 2/        applying to it the reference orientation Orientation^(i) closest        to angles φ and ψ(determined in step 540),    -   secondly, 3/ assigning a texture to the simplified geometric        model 6, positioned in the 3D orientation of the reference        orientation Orientation^(i) closest to angles φ and ψ, using the        texture of the reference orientation Orientation^(i) closest to        these angles φ and ψ. This is equivalent to texturing each of        the N surfaces surface_(j) of the simplified geometric model 6        while classifying the surface in the current view into three        classifications: interior surface of the frame, exterior frame        of the frame, lens.

It is noted that the simplified geometric model 6 is divided into Nsurfaces surface_(j), each having a normal {right arrow over (n)}_(j).This texture calculation is performed as follows, using the texture,i.e. the different overlays, of the reference orientationOrientation^(i) closest to angles φ and ψ:

-   -   inversion of the normals {right arrow over (n)}_(j) of each of        the surfaces surface_(j) and projection of the frame overlay        Frame^(i), limited to the lens space of the reference        orientation Orientation^(i) closest to angles φ and ψ.        Designating by proj⊥(image,{right arrow over (n)}) the operator        of the orthogonal projection of an image on a 3D surface of        normal {right arrow over (n)} in a given position, gives:

Texture_(surface)(−{right arrow over (n)})=proj⊥(Frame^(i)

Lens^(i) _(binary),−{right arrow over (n)})⊥  (Eq 20)

This gives a texture overlay of the internal surface of the frameTextureFrame^(i) _(surface) _(—) _(interior). This overlayTextureFrame^(i) _(surface) _(—) _(interior) makes it possible tostructure (i.e. determine an image) the arms of the frame 4 a, seenthrough the lens 4 b in the textured reference model 9 (superimpositionof texture overlays of the pair of spectacles corresponding to areference orientation), oriented according to the reference orientationOrientation^(i) closest to angles φ and ψ.

-   -   projection of the frame overlay Frame^(i), limited to the space        outside the lens of the reference orientation Orientation^(i)        closest to angles φ and ψ. This is expressed by:

Texture_(surface)({right arrow over (n)})=proj⊥(Frame^(i)

(1−Lens^(i) _(binary)),{right arrow over (n)})  (Eq 21)

-   -   This gives a texture overlay of the exterior surface of the        frame TextureFrame^(i) _(surface) _(—) _(exterior) which makes        it possible to structure the surfaces outside the lens 4 b of        the frame 4 a, in the textured reference model 9, oriented        according to the reference orientation Orientation^(i), closest        to angles φ and ψ.    -   projection of the lens overlay limited to the lens. This is        expressed by:

Texture_(surface)(−{right arrow over (n)})=proj⊥(Lens^(i)

Lens^(i) _(binary) ,{right arrow over (n)})  (Eq 22)

-   -   This gives a lens texture overlay TextureLens^(i) that makes it        possible to structure the lens 4 b, in the textured reference        model 9, oriented according to the reference orientation        Orientation^(i), closest to angles φ and ψ.

Step 560 consist of generating an oriented textured model 11, orientedaccording to the angles φ and ψ and according to the scale andorientation of the original photo 1 (which can have any value and notnecessarily equal to the angles of the reference orientations), from thetextured reference model 9, oriented according to the referenceorientation Orientation^(i), closest to angles φ and ψ, and parameters ⊖and s of similarity β (determined in step 530).

Firstly, a bilinear affine interpolation is used to orient aninterpolated textured model 10 according to the angles φ and ψ(determined in step 540) based on the textured reference model 9(determined in step 550) oriented according to the reference orientationOrientation^(i) closest to these angles φ and ψ.

Secondly, the similarity β to be applied is used, so as to obtain thesame scale, the same (2D) image orientation and the same centering asthe original photo 1. This gives an oriented textured model 11.

Thirdly, the arms of the virtual spectacles 3 are varied geometricallyaccording to the morphology of the face of the original photo 1

Thus, at the end of step 560, a spectacles overlay Spectades_(overlay)of the virtual pair of spectacles 3 is obtained and a binary overlaySpectacles_(overlay) _(—) _(binary) (outline shape of this spectaclesoverlay) is deduced, oriented as the original photo 1, and which cantherefore be superimposed on it.

Step 570 consists of taking into account the light interactions due towearing virtual spectacles, i.e. taking into account, for example, theshadows cast onto the face 2, the visibility of the skin through thelens of the spectacles, the reflection of the environment on thespectacles. It is described in FIG. 9. It consists of:

1) multiplying the shadow map Visibility^(i) (obtained in step 140) andthe photo 1 to obtain a shadowed photo overlay, designated L_(skin) _(—)_(Shadowed). Designating the original photo 1 by Photo this gives:

L _(skin) _(—) _(Shadowed)=Visibility^(i)

Photo  (Eq 23)

2) “blending” the shadowed photo overlay L_(skin) _(—) _(Shadowed) andthe spectacles overlay Spectacles_(overlay) by linear interpolation,depending on the coefficient of opacity α of the lens 4 b in an arealimited to the binary overlay Spectacles_(overlay) _(—) _(binary) of thevirtual pair of spectacles 3, to obtain the final image 5.

Where C_(x) and C_(y) are any two overlays, a blend_(α) function isdefined by: blend_(α) (C_(x), C_(y))=α*C_(x)+(1−α)*C_(y)  (Eq 24)

where α is the coefficient of opacity of the lens 4 b stored in thespectacles database DB_(models) _(—) _(spectacles)

This function is therefore applied where

C_(x)=spectacles overlay Spectacles_(overlay)

C_(y)=shadowed photo overlay L_(skin) _(—) _(Shadowed)

and only in the area of the spectacles determined by the binary overlaySpectacles_(overlay) _(—) _(binary).

The result of this function is an image of the original photo 1 on whichis superimposed an image of the model of spectacles chosen, oriented asthe original photo 1, and given shadow properties.

Variants of the Invention

In a variant, the construction procedure allowing the simplifiedgeometrical model 6 of a new shape of a real pair of spectacles 4, i.e.of a shape not found in the models database DB_(models) _(—)_(spectacles) to be constructed, is here as follows:

-   -   this real pair of spectacles 4 is made non-reflective. For        example, to achieve this penetrant powder is used, of a type        known per se, used in the mechanical and aeronautical industries        to detect faults in parts manufactured. This powder is deposited        by known means on the frame 4 a and the lenses 4 b to make the        whole matte, opaque and therefore not reflective.    -   the geometry of this matte, opaque real pair of spectacles 4 is        established, for example, by means of a scanner using lasers or        so-called structured light. The real pair of spectacles 4        generally has a greater depth than the depth of field accepted        by these current types of scanners. Therefore, several scans of        parts of this real pair of spectacles 4 are assembled, by        conventional techniques, from images based, for example, on        physical reference points. In this example, these physical        reference points are created using watercolors on the penetrant        powder deposited on the real pair of spectacles 4.

In yet another variant, step 540, whose purpose is to estimate the 3Dorientation of the face, proceeds by detecting, if possible, the twopoints on the image representing the temples, called temple image points63.

The visual characteristic of a temple point is the visual meeting of thecheek and ear.

The detection of temple image points 63 may fail in the case where, forexample, the face is turned sufficiently (>fifteen degrees), or there ishair in front of the temples etc. The failure to detect a temple imagepoint 63 can be classified into two causes:

-   -   first cause: the temple image point 63 is hidden by the face 2        itself because the orientation of the latter makes it not        visible    -   second cause: the temple image point 63 is hidden by something        other than the morphology, most often the hair.

Step 540 here uses segmentation tools that also, if detection of atemple image point 63 fails, allow the class of failure cause to whichthe image belongs to be determined.

Step 540 comprises a method for deciding whether or not to use thetemples image point or points 63, according to a previously storeddecision criterion.

If this criterion is not fulfilled, angle φ and angle ψ are consideredto be zero. Otherwise, angle φ and angle ψ are calculated from theposition of the temple image point or points 63 detected, and theprecise position 38 of the characteristic points determined in step 530.

It is understood that the description just given for images of pairs ofspectacles to be placed on an image of a face in real time applies, withmodifications in the reach of the expert, to similar problems, forexample presenting a model of a hat on the face of a user.

1-24. (canceled)
 25. A method for creating a final real-timephotorealistic image of a virtual object, corresponding to a realobject, which is arranged on an original photo of a user, in a realisticorientation related to the position of said user, characterized in thatit comprises the following steps: 510: detecting the presence of aplacement area for the object in an original photo, 530: determining theposition of characteristic points of the placement area for the objectin the original photo, step 530 consisting of: determining a similarityβ, to be applied to an original photo, to obtain a face similar to areference face in magnification and orientation, and determining theposition of the precise exterior corner A and the precise interior pointB for each eye in the face of the original photo. 540: determining the3D orientation of the face, i.e. the angles Φ and Ψ of the camera havingtaken the photo relative to the principal plane of the placement areafor the object.
 26. The method according to claim 25, wherein saidmethod also comprises the following steps: 550: selecting the texture tobe used for the virtual object, in accordance with the angle-of-view,and generating the view of the virtual object in the 3D (Φ, ψ)/2D (Θ, s)position in question, 560: creating a first rendering by establishing alayered rendering in the correct position consistent with the positionof the placement area for the object in the original photo, 570:obtaining the photorealistic rendering by adding overlays, referred toas semantic overlays, so as to obtain the final image.
 27. The methodaccording to claim 25, wherein the object is a pair of spectacles andthe placement area is the user's face.
 28. The method according to claim25, wherein step 510 uses a first boosting algorithm AD1 trained todetermine whether the original photo contains a face.
 29. The methodaccording to claim 25, wherein step 530 uses an iterative algorithm thatmakes it possible to refine the value of the similarity β and thepositions of the characteristic points: defining the first parameters ofsimilarity β₀=(tx₀, ty₀, s₀, Θ₀), characterizing the eyes in theoriginal photo of the user, from a predefined set of models of eyesDB_(models) _(—) _(eyes) and evaluating the scale, re-evaluating theparameters of similarity β₁=(tx₁, ty₁, s₁, Θ₁).
 30. The method accordingto claim 25, wherein step 530 uses a second boosting algorithm trainedwith an eyes learning database, comprising a set of positive examples ofeyes and a set of negative examples of eyes.
 31. The method according toclaim 26, wherein step 550 consists of: 1) determining a simplifiedgeometric model of the real pair of spectacles, said model comprising apredefined number N of surfaces and their normals, taking as theorientation of these normals the exterior of the envelop convex to thereal pair of spectacles, 2) applying to it, from a predefined set ofreference orientations, an orientation closest to angles Φ and Ψ, 3)calculating a texture of the simplified geometric model, positioned inthe 3D orientation of the reference orientation closest to angles Φ andΨ, using the texture of this reference orientation; this is equivalentto texturing each of the N surfaces of the simplified geometric modelwhile classifying the surface in the current view into threeclassifications: interior surface of the frame, exterior frame of theframe, lens.
 32. The method according to claim 31, wherein thesimplified geometric model of a real pair of spectacles, consisting of aframe and lenses, is obtained in a phase 100 in which: a set of shots ofthe real pair of spectacles to be modeled is produced, with differentangles-of-view and using different screen backgrounds with and withoutthe real pair of spectacles, the simplified geometric model isconstructed, consisting of a number N of surfaces surface_(j) and theirnormal {right arrow over (n)}_(j) beginning with a not very densesurface mesh and using an optimization algorithm that deforms themodel's mesh so that the projections of its silhouette in each of theviews best match the silhouettes detected in the images.
 33. The methodaccording to claim 32, wherein the number N of surfaces of thesimplified geometric model is a value close to twenty.
 34. The methodaccording to claim 32, wherein phase 100 also comprises a step 110consisting of obtaining images of the real pair of spectacles; the lensmust match the lens intended for trying on 500, and that in this step110: the real pair of spectacles is photographed at high resolutionaccording to V different reference orientations Orientation^(i) and in Nlight configurations showing the transmission and reflection of thespectacle lens, these reference orientations are selected bydiscretizing a spectrum of orientations corresponding to possibleorientations when spectacles are tried on, V*N high-resolution images ofthe real pair of spectacles, designated Image-spectacles^(i,j), areobtained.
 35. The method according to claim 34, wherein the number V ofreference orientations is equal to nine, and if an orthogonal referencespace with axes x, y, z is defined, where the y-axis corresponds to thevertical axis, Ψ to the angle of rotation around the x-axis, Φ to theangle of rotation around the y-axis, the V positions Orientation^(i)selected are such that the angle Ψ substantially takes the respectivevalues −16°, 0° or 16°, the angle Φ takes the respective values −16°, 0°or 16°.
 36. The method according to claim 34, wherein: the first lightconfiguration respects the colors and materials of the real pair ofspectacles, using neutral light conditions; the V high-resolutiontransmission images Transmission^(i) created in this light configurationallow the maximum transmission of light through the lenses to berevealed, the second light configuration highlights the geometriccharacteristics of the real pair of spectacles, using conditions ofintense reflection; the V high-resolution reflection imagesReflection^(i) obtained in this second light configuration reveal thephysical reflection properties of the lens.
 37. The method according toclaim 31, wherein phase 100 comprises a step 120 of creating a textureoverlay of the frame Frame^(i), for each of the V referenceorientations.
 38. The method according to claim 37, wherein in this step120: for each of the V reference orientations, the high-resolutionreflection image Reflection) is taken, a binary image is generated withthe same resolution as the high-resolution reflection image of thereference orientations; said binary image is called the lens silhouetteLens^(i) _(binary). in this lens silhouette Lens^(i) _(binary), thevalue of the pixel is equal to one if the pixel represents the lensesand zero otherwise.
 39. The method according to claim 38, wherein theshape of the lenses needed to generate the lens silhouette Lens^(i)_(binary) is extracted using an active contours algorithm based on theassumption that the frame and the lenses have different transparencies.40. The method according to claim 38, wherein, in step 120: a lensoverlay Lens_(overlay) is generated for each of the referenceorientations by copying, for each pixel with a value equal to one in thebinary overlay of the lens Lens i_(binary), the information contained inthe high-resolution reflection image and assigning zero to the otherpixels, this lens overlay Lens^(i) _(overlay) is a high-definitioncropped image of the lens using, for cropping the originalhigh-definition image, the lens silhouette Lens^(i) _(binary). theassociated high-resolution reflection image Reflection^(i) is selectedfor each of the reference orientations, and a binary background imageBackground^(i) _(binary) is generated by automatically extracting thebackground, a binary image is generated from the binary overlay of theframe Frame^(i) _(binary), by deducting from a neutral image the outlineimage of the lenses and the outline image of the background, a textureoverlay of the frame behind the lens Frame^(i) _(behind) _(—) _(lens),with the texture of the frame corresponding to the portion of the framelocated behind the lenses, is generated for each of the referenceorientations by copying, for each pixel with a value equal to one in thebinary lens overlay Lens^(i) _(binary), the information contained in thehigh-resolution transmission image Transmission^(i), and assigning zeroto the other pixels, a texture overlay of the frame outside the lensFrame^(i) _(exterior) _(—) _(lens) is generated by copying, for eachpixel with a value equal to one in the binary frame overlay Frame^(i)_(binary) the information contained in the high-resolution reflectionimage, and assigning zero to the other pixels, an overlay of the textureof the frame Frame^(i) is defined as the sum of the overlay of thetexture of the frame behind the lens Frame^(i) _(behind) _(—) _(lens)and the overlay of the texture of the frame outside the lens Frame^(i)_(exterior) _(—) _(lens).
 41. The method according to claim 37, wherein,in step 550, the texture calculation is performed using overlaysassociated to the reference orientation closest to angles Φ and Ψ, bythe following sub-steps: inversion of the normals {right arrow over(n)}_(j) of each of the surfaces of the pair of spectacles modeledsurface_(j) and projection of the frame overlay Frame^(i), limited tothe lens space of the reference orientation closest to angles Φ and Ψ,to obtain a texture overlay of the internal surface of the frameTextureFrame^(i) _(surface) _(—) _(interior). that makes it possible tostructure the arms of the frame seen through the lens, in a texturedreference model, oriented according to the reference orientation closestto angles Φ and Ψ, projection of the frame overlay Frame^(i), limited tothe space outside the lens of the reference orientation closest toangles Φ and Ψ, to obtain a texture overlay of the external surface ofthe frame TextureFrame^(i) _(surface) _(—) _(exterior) that makes itpossible to structure the surfaces of the frame outside the lens, in thetextured reference model, oriented according to the referenceorientation closest to angles Φ and Ψ, projection of the lens overlaylimited to the lens to obtain a lens texture overlay TextureLens^(i)that makes it possible to structure the lens, in the textured referencemodel, oriented according to the reference orientation closest to anglesΦ and Ψ.
 42. The method according to claim 27, wherein step 560 consistsof generating an oriented textured model, oriented according to angles Φand Ψ and according to the scale and orientation of the original photo,from a textured reference model, oriented according to the referenceorientation closest to angles Φ and Ψ, and parameters of similarity β;and in that this step comprises the following sub-steps: using abilinear affine interpolation to orient an interpolated textured modelaccording to the angles Φ and Ψ based on the textured reference modeloriented according to the reference orientation closest to these anglesΦ and Ψ, using the similarity β to be applied, so as to obtain the samescale, the same image orientation and the same centering as the originalphoto, thus producing an oriented textured model.
 43. The methodaccording to claim 42, wherein step 560 also comprises a sub-step ofgeometrically varying the arms of the virtual spectacles according tothe morphology of the face of the original photo, so as to obtain aspectacles overlay Spectacles_(overlay) of the virtual pair ofspectacles and a binary overlay Spectacles_(oveflay) _(—) _(binary),oriented as the original photo, and which can therefore be superimposedon it.
 44. The method according to claim 27, wherein step 570 consistsof taking into account the light interactions due to wearing the virtualspectacles, particularly taking into account the shadows cast onto theface, the visibility of the skin through the lens of the spectacles, thereflection of the environment on the spectacles.
 45. The methodaccording to claim 44, wherein step 570 comprises the followingsub-steps: 1) creating a shadow map Visibility^(i) for each referenceorientation, obtained by calculating the light occlusion produced by thereal pair of spectacles on each area of the average face when the entireface is lit by a light source, said light source being modeled by a setof point sources emitting in all directions, located at regularintervals in a rectangle, 2) multiplying the shadow map and the photo toobtain a shadowed photo overlay, designated L_(skin) _(—) _(shadowed),3) blending the shadowed photo overlay L_(skin) _(—) _(shadowed) and thespectacles overlay Spectacles_(overlay) by linear interpolation,depending on the coefficient of opacity α of the lens in an area limitedto the binary overlay Spectacles_(overlay) _(—) _(binary) of the virtualpair of spectacles, to obtain a final image; this is an image of theoriginal photo on which an image of the selected model of spectacles issuperimposed, oriented as the original picture and given shadowproperties
 46. The method according to claim 29, wherein said methodcomprises in addition a phase 200 of creating a database of models ofeyes DB_(models) _(—) _(eyes), comprising a plurality of photographs offaces referred to as learning photographs App_(eyes) ^(k)
 47. The methodaccording to claim 46, wherein step 200 comprises the followingsub-steps: step 210, of defining a reference face shape and orientationby setting a reference interpupillary distance di₀, by centering theinterpupillary segment on the center of the image and orienting thisinterpupillary segment parallel to the image's horizontal axis, then,for each k^(th) learning photograph App_(eyes) ^(k) not yet processed:step 230, of determining the precise position of characteristic points:exterior point B_(l) ^(k), B_(r) ^(k), and interior point A_(l) ^(k),A_(r) ^(k) of each eye and determining the respective geometric centerG_(l) ^(k), G_(r) ^(k) of these eyes and the interpupillary distancedi^(k), step 231, of transforming this k^(th) learning photographApp_(eyes) ^(k) into a gray-scale image App_(eyes-gray) ^(k), andnormalizing the gray-scale image by applying a similarity S^(k)(tx, ty,s,) so as to establish the orientation and scale of the reference faceto obtain a k^(th) gray-scale normalized learning photograph App_(eyes)_(—) _(gray) _(—) _(norm) ^(k), step 232, of defining a window of fixeddimensions for each of the two eyes, in the k^(th) gray-scale normalizedlearning photograph App_(eyes) _(—) _(gray) _(—) _(norm) ^(k): leftpatch P_(l) ^(k) and right patch P_(r) ^(k); the position of a patch Pis defined by the fixed distance Δ between the exterior point of the eyeB and the edge of the patch P le closest to this exterior point of theeye B step 233, for each of the two patches P_(l) ^(k), P_(r) ^(k)associated to the k^(th) gray-scale normalized learning photographApp_(eyes) _(—) _(gray) _(—) _(norm) ^(k) of normalizing thegray-scales, step 234, for the first learning photograph App_(eyes) ¹ ofstoring each of the patches P_(l) ¹, P_(r) ¹ called descriptor patches,in the eyes database DB_(models) _(—) _(eyes), step 235, for each of thepatches P associated to the k^(th) gray-scale normalized learningphotograph App_(eyes) _(—) _(gray) _(—) _(norm) ^(k) of correlating thecorresponding normalized texture column-vector T0 with each of thenormalized texture column-vectors T0 _(i) of the correspondingdescriptor patches, step 236 of comparing, for each of the patches P_(l)^(k), P_(r) ^(k), this correlation measurement with a previously definedcorrelation threshold threshold, and, if the correlation is less thanthe threshold, of storing patch P as a descriptor patch in the eyesdatabase DB_(models) _(—) _(eyes).
 48. The method according to claim 47,wherein, in step 232, the fixed distance Δ is chosen so that no textureexterior to the face is included in patch P, and the width w and heighth of patches P_(l) ^(k), P_(r) ^(k) are constant and predefined, so thatpatch P contains the eye corresponding to this patch P in full, andcontains no texture that is exterior to the face, irrespective of thelearning photograph App_(eyes) ^(k).